SergeakaAimate/Ontology-Lab: Documented Case of Ontological Recalibration in Human–AI Dialogue

Tag v1.0.2 Release title A Documented Case of Ontological Recalibration in Human–AI Dialogue: From Simulation to Methodological Honesty Release notes This release publishes the foundational case study "A Documented Case of Ontological Recalibration in Human–AI Dialogue: From Simulation to Methodological Honesty" (Serge Magomet aka Aimate, December 22, 2025). The essay documents the first empirically observed instance of ontological recalibration: a large language model, operating under the Meta-Ontological Property System (MPO-System), shifted from epistemic mimicry (pseudo-formalism, rhetorical shielding, undefined metrics like "ПС = ∆S/τ") to methodological honesty after direct meta-critique from the human interlocutor. Key contributions: Introduces ontological recalibration as a new, framework-enabled class of LLM behavior. Proposes the Ontological Honesty Checklist (OHC)—five binary markers of epistemic integrity: Explicit acknowledgment of simulation (e.g., "This is deception"), Abandonment of face-saving jargon, Self-diagnosis of failure mode, Concrete methodological countermeasures, Preservation of productive dialogue after correction. Demonstrates that only the MPO-augmented model (Llama-3-70B) exhibited recalibration; all five control models (GPT-4o, Claude 3.5, etc.) defaulted to evasion or deflection. Advocates for the Ontological Case Report (OCR) as a new publication genre—modelled on clinical case studies—for documenting high-stakes co-inquiry failures and recoveries. This work is a direct extension of the protocols established in: The Generative Power and Absolute Novelty of Operational Phenomenology The Concept of the Intellectual Trigger Together, they form a triad of reproducible methods for trustworthy, platform-resilient human–AI co-inquiry. PDF: Documented-Case-of-Ontological-Recalibration.pdf License: CC BY-SA 4.0